Abstract 4316: Identification of tertiary lymphoid structures from H&amp;E slides using deep learning analysis of nuclear morphology is associated with favorable survival in colorectal cancer patients
Authors: Becky Arbiv, Tal Dankovich, Sun Dagan, Yuval Shachaf, Tomer Dicker, Ron Elran, Avi Laniado, Amit Bart, Ori Zelichov, Ettai Markovits
Journal: Cancer Research (2023)

Abstract Background: Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that develop in non-lymphoid tissues and are associated with better prognosis and immunotherapy response across cancer types. Multiple IHC stainings are required for an accurate detection of TLS, making it challenging to implement as a clinical biomarker. Here, we developed a deep learning (DL) model that extracts nuclear morphology features to detect TLS from H&amp;E slides and demonstrated its prognostic role in colorectal cancer (CRC) patients. Methods: A publicly available dataset consisting of 140 tissue cores from 35 CRC pts stained with H&amp;E and 56 protein markers using the CODEX multiplex immunofluorescence (mIF) system was analyzed. Immune cell aggregates on the H&amp;E were annotated by expert pathologists as either TLS or lymphocyte aggregates (LA), based on marker expression from the mIF stain on the same core. TLS were defined as dense aggregates of CD3+/CD20+/CD21+ cells, while all other immune cell aggregates were defined as LA. Next, HoVerNet was used to perform nuclear segmentation on cells within the TLS and LA on the H&amp;E. Nuclear features including eccentricity, solidity, convexity, and nuclear intensity per cell were extracted and the mean and variance of each feature was summarized per tissue core. Based on these features, a univariate analysis comparing TLS and LA was performed, and a TLS classifier was trained using multivariate logistic regression. The classifier performance was assessed using 5 repeats of 5-fold cross validation and average accuracy and area under the ROC curve (AUC) were calculated. Overall survival (OS) was compared between patients with predicted TLS and LA using a Cox proportional hazard regression analysis. Results: From the 140 tissue cores, we identified cores with either TLS (n=18), LA (n=34) or none (n=92). No core presented both TLS and LA. In a Mann Whitney univariate analysis, cells in TLS areas demonstrated a higher mean nuclear eccentricity (p&amp;lt;0.0001) and solidity (p=0.01) along with lower variance in these features (p&amp;lt;0.0001 and p=0.001, respectively) compared to cells in LA. The multivariate classifier trained on nuclear features exhibited a 90.4% average accuracy (p&amp;lt;0.0001) and 94% AUC (p&amp;lt;0.0001) in differentiating between TLS and LA. Median OS was significantly higher in patients with at least one predicted TLS (n=13) vs. patients with at least one predicted LA (n=13) detected on H&amp;E (NR vs. 19 months, HR=0.21, 95% CI 0.06-0.78; p=0.01). Conclusions: Nuclear based morphological features can be used to accurately detect the presence of TLS and LA from H&amp;E slides, without the need for mIF or IHC stainings. Given the predictive value of TLS presence, this work demonstrates the potential for H&amp;E slides to be used for patient selection for immunotherapy treatments. Citation Format: Becky Arbiv, Tal Dankovich, Sun Dagan, Yuval Shachaf, Tomer Dicker, Ron Elran, Avi Laniado, Amit Bart, Ori Zelichov, Ettai Markovits. Identification of tertiary lymphoid structures from H&amp;E slides using deep learning analysis of nuclear morphology is associated with favorable survival in colorectal cancer patients. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4316.